Batterman Stuart, Xu Lizhong, Chen Feng, Chen Fang, Zhong Xuefen
School of Public health, University of Michigan, Ann Arbor, Michigan, 48104, US.
College of Environmental Science and Engineering, Fujian Normal University, Fuzhou, 350007, China.
Atmos Environ (1994). 2016 Nov;145:104-114. doi: 10.1016/j.atmosenv.2016.08.060. Epub 2016 Aug 23.
High concentrations of particulate matter (PM) and frequent air pollution episodes in Beijing have attracted widespread attention. This paper utilizes data from the new air pollution network in China to examine the current spatial and temporal variability of PM at 12 monitoring sites in Beijing over a recent 2-year period (April 2013) to March 2015). The long term (2-year) average concentration was 83 µg·m, well above Chinese and international standards. Across the region, annual average concentrations varied by 20 µg·m (25% of the average level), with lower levels in suburban areas compared to periurban and urban areas, which had similar concentrations. The spatial variation in PM concentrations was associated with several land use and economic variables, including the fraction of vegetated land and building construction activity, which together explained 71% of the spatial variation. Daily air quality was characterized as "polluted" (above 75 µg·m) on 36 to 47% of days, depending on site. There were 77 pollution episodes during the study period (defined as two or more consecutive days with Beijing-wide 24-hour average concentrations over 75 µg·m), and 2 to 5 episodes occurred each month, including summer months. The longest episode lasted 9 days and daily concentrations exceeded 450 µg·m. Daily PM levels were autocorrelated (r = 0.516) and associated with many meteorological variables, including barometric pressure, relative humidity, hours of sunshine, surface and ambient temperature, precipitation and scavenging coefficient, and wind direction. Parsimonious models with meteorological and autoregressive terms explained over 60% of the variation in daily PM levels. The first autoregressive term and hours of sunshine were the most important variables in these models, however, the latter variable is PM-dependent and thus not an explanatory variable. The present study can serve as a baseline to compare the improved air quality in Beijing expected in future years.
北京高浓度的颗粒物（PM）以及频繁的空气污染事件引起了广泛关注。本文利用中国新的空气污染监测网络数据，研究了北京12个监测点在最近两年期间（2013年4月至2015年3月）PM的时空变化情况。长期（两年）平均浓度为83微克·立方米，远高于中国和国际标准。在整个区域内，年平均浓度相差20微克·立方米（占平均水平的25%），郊区浓度低于城乡结合部和城区，而城乡结合部和城区浓度相近。PM浓度的空间变化与多个土地利用和经济变量有关，包括植被覆盖土地比例和建筑施工活动，这些因素共同解释了71%的空间变化。根据监测点不同，每日空气质量有36%至47%的天数被判定为“污染”（高于75微克·立方米）。研究期间共有77次污染事件（定义为全市24小时平均浓度连续两天或以上超过75微克·立方米），每月发生2至5次，包括夏季月份。最长的一次污染事件持续了9天，日浓度超过450微克·立方米。每日PM水平存在自相关性（r = 0.516），并与许多气象变量有关，包括气压、相对湿度、日照时长、地表和环境温度、降水量和清除系数以及风向。包含气象和自回归项的简约模型解释了每日PM水平变化的60%以上。在这些模型中，第一个自回归项和日照时长是最重要的变量，然而，后一个变量依赖于PM，因此不是一个解释变量。本研究可作为一个基线，用于比较未来几年北京空气质量的改善情况。
